Medical decision support systems utilizing gene expression and clinical information and method for use

ABSTRACT

Embodiments of this invention provide improved medical decision support systems and methods for using such systems to simultaneously consider two or more different types of information along with estimates of accuracies of the information to produce a combined predictor. Such predictors have greater accuracy compared to use of the individual types of information alone. Increased accuracy can increase the likelihood of correct diagnosis and/or evaluation of clinical condition or outcome, and can decrease the frequency of false negative results, including misdiagnosis. Embodiments of medical decision support systems can include EFuNN, Bayesian or other statistical estimators to produce a combined predictor. The systems can be used to extract relationship rules between sets of genes and clinical variables common for patients of a group, thus making a personalized gene-based treatment possible. Such systems are incorporated into computer-based devices and are run using suitable computer programs. Outputs can be directed to hard-copy devices for printing, or can be transmitted remotely to a terminal at a location where a practitioner is interacting with a patient.

RELATED APPLICATION

This application claim priority to U.S. Provisional Patent Application Ser. No: 60/403,756, filed Aug. 15, 2002, incorporated herein fully by reference.

FIELD OF THE INVENTION

This invention relates to diagnosis and evaluation of disease. In particular, this invention relates to systems for supporting medical decisions based on multiple sets of information relating to a patient's condition. More particularly, this invention relates to systems for supporting medical decisions based on genetic information and clinical information.

BACKGROUND

Medical diagnosis and evaluation of a patient's condition are of great concern to medical professionals. Much time is spent on obtaining information from a patient during visits to practitioners. Medical history is often a major component of a proper diagnosis. Additionally, a practitioner may request specific physiological or pathophysiological measurements be made, to facilitate understanding the patient's condition. Such clinical information has historically been a powerful tool to provide proper diagnosis and evaluation of therapy.

With the advent of increasingly widespread acquisition of genetic information from a patient, a practitioner now has an opportunity to incorporate genetic information and clinical information together, to further improve accuracy of diagnosis and evaluation of therapy. Proper diagnosis and monitoring are necessary for a practitioner to be able to make the best informed decisions about either initiating a course of therapy or for altering a therapeutic regimen to best suit a particular patient's needs.

However, there are few systems available that can be used to incorporate genetic information with a patient's clinical information to provide the practitioner with rapid, reliable information to assist in the decision making process.

In order to influence patient management in a clinical environment, medical decision support systems must have a high level of confidence. Examples of such medical systems are:

-   -   Classification systems that classify, for example, a new tumour         (a new patient's data) into existing classes of diseases;     -   Prognostic systems that predict the risk of a patient for a         particular disease (e.g. cardiovascular risk prognosis); and     -   Prognostic systems that predict the outcome of a treatment for a         particular patient and a particular drug (e.g. the outcome of a         chemotherapy for a patient of DLBCL cancer, for example, see         reference 1)

Many existing medical decision support systems use only one source of information, and thus the confidence of their models is not very high. For example, Shipp et al [1] described a system that uses machine learning techniques, specifically a weighted voting algorithm and support vector system, for prognostic stratification of patients based on gene expression data taken from 58 samples of 32 cured and 26 fatal cases. However their approach misclassified 23% of the patients in terms of predicting the outcome of their chemotherapy treatment. They achieved 77.6% correct prognosis of both cured and fatal cases of B-cell lymphoma cancer. The models on a similar task presented in Alizadeh et al [2] are not clinically applicable for classification purposes. Thus, there is a need for improved reliability of medical decision support systems that can overcome the shortcomings in the art.

In another aspect of the problem, there are no existing methods that help discover relationship between gene expression and clinical parameters thus making a personalised treatment of patients with different gene expression profiles according to their clinical parameters (e.g. age). It is known that some genes change their expression activity in one person over time and in different environments. Thus, there is a need for methods and systems that facilitate the discovery of related gene profiles to clinical data.

SUMMARY

Embodiments of this invention include novel methods for increasing the confidence of medical decision support systems by developing independent models based on (1) gene expression data, and (2) on clinical information, and combining them at a higher level. The method is extendable with the addition of other sources of information (e.g. demographic). The invention is also concerned with a novel method for the discovery of relationship between gene expression patterns and clinical parameters thus making a personalised (or a clinical group specific) treatment possible.

We have improved the prediction accuracy of a medical decision support system with the use of multiple types or classes of information about a subject's or patient's condition. In certain embodiments, gene expression information and the available clinical information are used to diagnose disease and to predict outcomes. In general, we can use different types of classifiers/predictors that relate to different sources of information or different classes of information. Each of the classifiers may be obtaining good results for part of the overall problem, for example, for a particular class, but the combination of them provides better accuracy than any of them used individually.

BRIEF DESCRIPTION OF THE FIGURES

This invention is described with reference to specific embodiments thereof. A more complete understanding of the systems and methods can be appreciated by referring to the Figures, in which:

FIG. 1 depicts a general scheme of the invention for integrating gene expression information and clinical data.

FIG. 2 depicts the structural diagram of an EfuNN suitable for use with the methods of the invention.

FIG. 3 a depicts schematically a Venn diagram showing how two models, gene information and clinical information, can have different accuracy depending upon grouping of samples.

FIG. 3 b depicts a schematic diagram of how gene expression information and clinical information are correlated according to this invention in a hierarchical fashion with each other to produce a medical decision.

FIG. 4 depicts relationships between different values of Beta 1 and Beta 2 in a combined model.

FIG. 5 depicts a graph of accuracy of a medical decision as a function of alpha in a model of this invention.

FIG. 6 depicts a segment from the structure of a multiplayer perceptron (MLP) of this invention.

DETAILED DESCRIPTION

Medical Decision Support Systems

A combined model for an improved decision support system includes in general of three modules, one that operates on independent microarray gene expression information, another that operates on independent clinical information, and a third that operates on integrated gene expression and clinical information for each patient. A system may contain one, two, or three of the described modules, but it can also contain more modules if more sources of information are available.

FIG. 1 depicts a block diagram the flow of information in a decision support system that utilizes two sources of information, gene expression data and clinical information, for making a prognosis of the outcome of a disease and its possible treatment. The first flow of information is used in a first classifier/predictor module that is based on gene expression data. In certain embodiments the first classifier/predictor module can include EFuNN or Bayesian tools. The second flow of information is processed in a classifier/predictor module based on clinical information only. In certain embodiments, such classifier/predictor module can include an EFuNN or Bayesian tool. For a new patient, if both gene and clinical information is available, they are entered into the corresponding modules and the results are combined in a higher-level decision module using one or several models integrated at a higher level as it is described further in the invention.

This higher-level integration module combines information from two or more lower modules to produce the final prognosis for the outcome of the disease for this particular patient. Based on the suggested by the system prognosis, the best available treatment is selected.

It can be appreciated that other systems based on multiple-class analysis can be developed using the fundamental methods of this invention. The number of classifier/predictor modules can depend on the number of different classes or types of information available.

An illustration of the method is given in FIGS. 2-6 where two modules are used, one based on microarray gene expression data, and the other based on clinical information (in this case indicated as IPI). FIG. 2 depicts an embodiment of an evolving connectionist structure (ECOS) evolving fuzzy neural network (EFuNN).

Evolving Connectionist Systems

In certain embodiments, the practitioner uses an adaptive learning, evolving connectionist systems (ECOS), in particular—an evolving fuzzy neural network system EFuNN, and also uses algorithms for gene expression profile (rule) extraction from ECOS [3, 4] and applies the proposed novel methodology for combining gene expression processing systems with clinical information processing systems as it is described further in the invention. The method allows for different modes of combination of gene expression and clinical data, as well as for adding new data and modules with time thus adjusting and improving the system. With the use of the proposed method the accuracy of the prognosis increases.

Using one of the modes of integration, namely an evolving connectionist system (“ECOS”) trained on the integrated input vector that combines both gene expression and clinical information, rules can be extracted that relate gene expression with clinical information, so that a personalised treatment can be applied for patients in a certain group of clinical parameters (e.g. age) having a particular pattern of gene expressed in their tissue. Evolving connectionist systems are multi-modular, and can be especially useful as architectures that facilitate modelling of evolving processes and knowledge discovery. They are described further in PCT, WO 01/78003, incorporated herein fully by reference.

Briefly, an ECOS may consist of many evolving connectionist modules. An ECOS is a neural network system that operates continuously in time and adapts its structure and functionality through a continuous interaction with the environment and with other systems according to: (i) a set of parameters P that are subject to change during the system operation; (ii) an incoming continuous flow of information with unknown distribution; (iii) a goal (rationale) criteria (also subject to modification) that is applied to optimise the performance of the system over time [7].

Evolving connectionist systems can have the following specific characteristics:

-   -   (1) They can evolve in an open space, not necessarily of fixed         dimensions.     -   (2) They can learn in on-line, pattern mode, incremental         learning, fast learning—possibly by one pass of data         propagation.     -   (3) They can learn in a life-long learning mode.     -   (4) They can learn as both individual systems, and evolutionary         population systems.     -   (5) They can have evolving structures and use constructive         learning.     -   (6) They can learn locally and locally partition the problem         space, thus allowing for a fast adaptation and tracing the         evolving processes over time.     -   (7) They can facilitate different kinds of knowledge, mostly         combined memory based, statistical and symbolic knowledge.

Two distinct phases are in an ECOS operation. During a first learning phase, data vectors are fed into the system one by one with their known output values. In a second phase (recall), a new vector is presented to the system and it calculates the output values for it.

There are different models of ECOS. One model, evolving fuzzy neural networks (EFuNN), is presented in FIG. 2. This model can be used for both classification tasks and prediction tasks.

While a “neural network module” may refer to any neural network satisfying the requirements of the aspects of the invention the use of an ECOS neural network is desirably used in certain embodiments. In some of these embodiments, a neural network is exemplified in the PCT publication WO 01/78003 (incorporated herein by reference). The algorithm describing the neural network is described further in WO 01/78003, incorporated fully by reference and is set out schematically below.

EFuNN Architecture

EFuNNs useful for embodiments of this invention can have a five-layer structure (FIG. 2). Nodes and connections are created/connected as data examples are presented. An optional short-term memory layer can be used through a feedback connection from the rule (also called, case) node layer. The layer of feedback connections could be used if temporal relationships of input data are to be memorized structurally.

The input layer represents input variables. The second layer of nodes (fuzzy input neurons, or fuzzy inputs) represents fuzzy quantification of each input variable space. For example, two fuzzy input neurons can be used to represent “small” and “large” fuzzy values. Different membership functions (MF) can be attached to these neurons (e.g., triangular, Gaussian, etc. [6, 7]. The number and the type of MF can be dynamically modified. The task of the fuzzy input nodes is to transfer the input values into membership degrees to which they belong to the corresponding MF. The layers that represent fuzzy MF are optional, as a non-fuzzy version of EFuNN can also be evolved with only three layers of neurons and two layers of connections.

The third layer contains rule (case) nodes that evolve through supervised and/or unsupervised learning. The rule nodes represent prototypes (exemplars, clusters) of input-output data associations that can be graphically represented as associations of hyper-spheres from the fuzzy input and the fuzzy output spaces. Each rule node r is defined by two vectors of connection weights—W1(r) and W2(r), the latter being adjusted through supervised learning based on the output error, and the former being adjusted through unsupervised learning based on similarity measure within a local area of the problem space. A linear activation function, or a Gaussian function, is used for the neurons of this layer.

The fourth layer of neurons represents fuzzy quantization of the output variables, similar to the input fuzzy neuron representation. Here, a weighted sum input function and a saturated linear activation function is used for the neurons to calculate the membership degrees to which the output vector associated with the presented input vector belongs to each of the output MFs. The fifth layer represents the values of the output variables. Here a linear activation function is used to calculate the defuzzified values for the output variables.

A partial case of EFuNN would be a three layer network without the fuzzy input and the fuzzy output layers. In this case a slightly modified versions of the algorithms described below are applied, mainly in terms of measuring Euclidean distance and using Gaussian activation functions.

Evolving learning in EFunNs is based on either of the following two assumptions:

-   (1) No rule nodes exist prior to learning and all of them are     created (generated) during the evolving process; or -   (2) There is an initial set of rule nodes that are not connected to     the input and output nodes and become connected through the learning     (evolving) process. The latter case is more biologically plausible     as most of the neurons in the human brain exist before birth, and     become connected through learning, but still there are areas of the     brain where new neurons are created during learning if     “surprisingly” different stimuli from previously seen are presented.     The EFuNN evolving algorithm presented in FIG. 2 b does not make a     difference between these two cases.

Each rule node, e.g. r_(j), represents an association between a hyper-sphere from the fuzzy input space and a hyper-sphere from the fuzzy output space (see FIG.2 a), the W1(r_(j)) connection weights representing the co-ordinates of the centre of the sphere in the fuzzy input space, and the W2 (r_(j))—the co-ordinates in the fuzzy output space. The radius of the input hyper-sphere of a rule node r_(j) is defined as R_(j)=1−S_(j), where S_(j) is the sensitivity threshold parameter defining the minimum activation of the rule node r_(j) to a new input vector x from a new example (x,y) in order the example to be considered for association with this rule node.

The pair of fuzzy input-output data vectors (x_(f),y_(f)) will be allocated to the rule node r_(j) if x_(f) falls into the r_(j) input receptive field (hyper-sphere), and y_(f) falls in the r_(j) output reactive field hyper-sphere. This is ensured through two conditions, that a local normalised fuzzy difference between x_(f) and W1(r_(j)) is smaller than the radius R_(j), and the normalised output error Er=||y−y′||/Nout is smaller than an error threshold E. Nout is the number of the outputs and y′ is the produced by EFuNN output. The error parameter E sets the error tolerance of the system.

Another example of a neural network module for some aspects of the invention is an evolving classification function (“ECF”), which can be used to classify data. The learning sequence of each iteration of an ECF is described in the following steps:

-   1) if all vectors have been inputted, finish the current iteration;     otherwise, input a vector from the data set and calculate the     distances between the vector and all rule nodes already created; -   2) if all distances are greater than a max-radius parameter, a new     rule node is created the position of the new rule node is the same     as the current vector in the input data space and its radius is set     to the min-radius parameter, and then go to step 1; otherwise: -   3) if there is a rule node with a distance to the current input     vector less then or equal to its radius and its class is the same as     the class of the new vector, nothing will be changed and go to step     1; otherwise: -   4) if there is a rule node with a distance to the input vector less     then or equal to its radius and its class is different from those of     the input vector, its influence field should be reduced. The radius     of the new field is set to the larger value from the distance minus     the min-radius, and the min-radius. -   5) if there is a rule node with a distance to the input vector less     then or equal to the max-radius, and its class is the same to the     vector's, enlarge the influence field by taking the distance as the     new radius if only such enlarged field does not cover any other rule     node which has the different class; otherwise, create a new rule     node the same way as in step 2, and go to step 1.

A recall (classification phase of new input vectors) in ECF is performed in the following way:

-   1) if the new input vector lies within the field of one or more rule     nodes associated with one class, the vector belongs to this class; -   2) if the input vector lies within the fields of two or more rule     nodes associated with different classes, the vector will belong to     the class corresponding the closest rule node. -   3) if the input vector does not lie within any field, then there are     two cases: (1) one-of-n mode: the vector will belong to the class     corresponding the closest rule node; (2) m-of-n mode: take m highest     activated by the new vector rule nodes, and calculate the average     distances from the vector to the nodes with the same class; the     vector will belong to the class corresponding the smallest average     distance.

The above-described ECF for classification has several parameters that need to be optimized according to the data set used. These are:

-   1) maximum radius -   2) minimum radius -   3) number of membership functions (mf) -   4) m-of-n value -   5) number of iterations for the data presentation during learning     phase.

These parameters can be optimized with the use of evolutionary computation methods, or other statistical methods, as described in [7].

An important characteristic of ECOS is that they can be used to extract rules that associate input variables (e.g. genes) to output variables (e.g. class categories). Each node in the hidden layer of the ECOS represents the center of a cluster of similar samples and can be expressed semantically as a rule. Each rule relates to the pattern of input feature levels for one or more samples belonging to a particular class from the data set. An example of what a rule might look like when extracted from the EFuNN is shown below: IF VAR1 is LOW (0.80) and VAR3 is HIGH (0.76) and VAR12 is HIGH (0.91) and VAR25 is LOW (0.80) and VAR31 is LOW (0.87) and . . . THEN CLASS_Z is VERY LIKELY (with a membership degree of 0.92), accommodated Training Examples in this rule are 10 out of 50, Radius of the cluster for this rule is 0.15.

The rules are then analysed in order to identify a set of variables that are significant in distinguishing between classes. The rule extraction method described above and in reference [3] can be applied to gene expression profiling of disease as described herein and in reference [4], to find patterns of significantly expressed genes in a cluster of diseased tissues. We have unexpectedly found that ECOS described in PCT WO 01/78003 [3], and the profiling method described in PCT/480030 [4], are particularly suited for complex disease profiling based not only on gene expression information, but on a variety of information sources, including gene expression data, protein data, clinical data (for example IPI (International Prognostic Index) number (e.g., see [1]) etc. Here we combine these sources of information through ECOS to create new, more efficient prognostic and classification systems for medical applications. Using these new systems and methods enable one to discover hidden relationships between sets of genes and clinical information previously unidentifiable.

EFuNN Learning Algorithm

To implement an EFuNN learning algorithm to the methods and systems of this invention, set initial values for the system parameters: number of membership functions; initial sensitivity thresholds (default Sj=0.9); error threshold E; aggregation parameter Nagg—number of consecutive examples after each aggregation is performed; pruning parameters OLD an Pr; a value for m (in m-of-n mode); maximum radius limit Rmax; thresholds T₁ and T₂ for rule extraction

Set the first rule node r₀ to memorise the first example (x,y): W1(r₀)=x_(f), and W2(r₀)=y_(f)′ Loop over presentations of new input-output pairs (x,y) { Evaluate the local normalised fuzzy distance D between x_(f) and the existing rule node connections W1 (formulae (1)) Calculate the activation A1 of the rule node layer. Find the closest rule node r_(k) (or the closest m rule nodes in case of m-of-n mode) to the fuzzy input vector x_(f) for which A1(r_(k)) >= S_(k) (sensitivity threshold for the node r_(k)), if there is no such a node, create a new rule node for (x_(f), y_(f)) else Find the activation of the fuzzy output layer A = W2.A1(1- D(W1,x_(f)))) and the normalised output error Err = || y − y′||/ Nout. ifErr > E  create a new rule node to accommodate the current example (x_(f), y_(f)) else Update W1 (r_(k)) and W2 (r_(k)) according to (2) and (3) (in case of m-of-n system update all the m rule nodes with the highest A1 activation). Apply aggregation procedure of rule nodes after each group of N_(agg) examples are presented. Update the values for the rule node r_(k) parameters S_(k), R_(k), Age(r_(k)), TA (r_(k)). Prune rule nodes if necessary, as defined by pruning parameters. Extract rules from the rule nodes ( }

In certain embodiments, EFuNN techniques have certain advantages when compared with the traditional statistical and neural network techniques, including: (i) they can have a flexible structure that reflects the complexity of the data used for their training; (ii) they can perform both clustering and classification/prediction; (iii) models can be adapted on new data without the need to be retrained on old data; (iv) they can be used to extract rules (profiles) of different sub-classes of samples.

FIG. 3 a illustrates the method from FIG. 1 in the notation of the set theory, where two modules C1 and C2 are used to classify two-class data (fatal and cured as described in [1]). Two models, in this case, one based on microarray gene expression data and the other on clinical information (in this case indicated as IPI), may have different accuracy depending on the grouping of the data samples. For example, 13 samples are predicted correctly only by module 1, 8 samples are predicted correctly by only module 2, and 33 samples are predicted correctly by the two modules. In this case, only two modules and two classes are used, but it can be appreciated that any number of modules and any number of classes can be used. The correctly predicted 54 samples out of 58 used in this case sets a boundary of 93% possible classification that could be eventually achieved with the use of this model.

FIG. 3 b depicts a block diagram of the method from FIG. 1, applied on a two-class problem (class and A and class B as is the case described in [1]) is depicted in FIG. 3 a. Two modules C1 and C2 from FIG. 3 a for the classification of the two classes A and B are combined in FIG. 3 b in a hierarchical manner.

FIG. 3 b depicts a three-layered system of this invention. The two modules from FIG. 1 are combined in a hierarchical manner. For example, the first module that operates on microarray gene expression data uses EFuNN, and the second module that operates on clinical (IPI in the case) information is a Bayesian classifier. In principle, for each of these modules any type of classifier/predictor can be used (e.g. neural networks, support vector machines, rule-based systems, decision trees, statistical methods and the like), and in some embodiments, an ECOS of the EFuNN or ECF type as described in this invention above.

It can be appreciated that additional layers can be applied depending on the numbers of modules and the types of classifiers or predictors available to be used. For example, in other embodiments, neural networks, support vector machines, rule-based systems, decision trees, and statistical methods can be used to construct a more complex medical decision support system. Thus, it can be appreciated that medical decision support systems can have more than three layers.

A first layer constitutes the modules themselves, each of them trained and tested with parameters optimised on the different data sets available (the different sources of information). It may be desirable to try different classification/prediction models for each of the modules and then chose the best one in terms of smallest residual error. Error minimization methods are well known in the art and need not be described further herein.

A second layer constitutes classes. All the class-elements of the second layer are fully connected with the module-elements of the first layer. A third layer in this example is the final outcome element, a combined output from all class-elements of the second layer. The third layer element is connected fully to the previous layer elements.

Elements from the different layers are connected through connection weights β1, 1-β1, β2, 1-β2, and α as shown in FIG. 3 b. To evaluate and quantify parameter values β1, β2, and α for the combined decision support system from FIG. 3 b, at least three methods can be used.

(1) The first method is based on an exhaustive search in the parameter values space, so for every combination of the parameter values a new system is generated and tested. The parameter values that give the system the highest accuracy are selected for the use in the medical decision support system in a clinical environment. FIG. 4 shows the accuracy of the combined system for different values of β1 and β2. We have found that the best accuracy is achieved for certain interval values for β1 and β2. The selected values are β1=β2=0.75.

FIG. 4 depicts results of such an exhaustive search method. Different values of Beta1 and Beta2 in the combined model (see FIG. 2) give different accuracy of prediction. The optimal values can be found through exhaustive search. During the exhaustive search procedure, all values for α are tested as shown in FIG. 4. The figure shows the process of testing the exhaustive search method for finding an optimum value for α the example model from FIG. 3. A value of 0.4 was found to produce desirably improved results compared to prior methods.

FIG. 5 depicts the results applying the exhaustive search method for finding an optimum value for a for the example model from FIG. 2 using the following method.

-   Step 1. Create 2 modules, one for gene expression data and one for     clinical information data (in case of only two modules used) through     training, testing, and parameter optimisation. -   Step 2. For every value of β1 & for every value of β2 & for every     value of α, DO Test the accuracy of the combined system on the whole     data set. -   Step 3. Chose the parameter values β1, β2, and α that give the     highest accuracy of the combined system.

EXAMPLE OF THE FIRST APPROACH

For the B-Lymphoma case study [1], values for the IPI (International Prognostic Index) are available for each of the 58 samples. Samples were stratified into 5 strata according to their IPI as follows:

-   -   Stratum 1: IPI=low: 26 samples, of which 19 belong to the cured         class and 7 to the fatal class.     -   Stratum 2: IPI=low intermediate: 11 samples, of which 7 belong         to the cured class and 4 to the fatal class.     -   Stratum 3: IPI=high intermediate: 17 samples, of which 4 belong         to the cured class and 13 to the fatal class.     -   Stratum 4: IPI=high: 2 samples, of which 2 belong to the fatal         class.     -   Stratum 5: IPI=unknown: 2 samples. These samples were assigned         the same IPI number as the closest samples based on the gene         expression data and on the Euclidean distance.

Using the IPI stratification, a single prediction factor and a Bayesian classifier gives a prediction accuracy of 73.2%. Using gene expression data and an EFuNN classifier gives an accuracy of 78.5%. The combined model accuracy, for β1=β2=0.75 and α=0.4, gives an overall accuracy of 87.5% if 56 examples are used for the first module. If the number of the IPI examples increases the overall accuracy of the first module increases because the IPI module is independent from the gene expression module (see reference [1]).

(2) The second method is a statistically based specialization method used for the selection of optimal parameter values. Each class output of each module is weighted with the normalised class accuracy calculated for this module across all the modules in the system. Continuous output values for the class outputs (e.g. 0.8 rather than 1) are multiplied by the weights, and the sum of the weighted output values from all modules constitutes the final output value for the class. A class is chosen with the highest output value. This is similar to the principle of statistically based specialisation [5]. The method is illustrated on the following example.

EXAMPLE OF THE SECOND APPROACH

Step 1: Assume that the combined model consists of three modules:

-   -   1) A gene expression data module (e.g. N genes as inputs and 2         classes as outputs, class A and class B);     -   2) A clinical information module (e.g. M inputs, binary encoding         of the clinical information, and 2 classes as outputs); and     -   3) An integrated information module (e.g. N+M inputs: the N gene         expression variables plus the M clinical input variables and 2         class outputs)

Step 2: Assume that each of the three modules produces different prognostic accuracy as follows: module one: 90% (88 and 92 for each class); module two: 70% (65 and 75 for each class respectively) and module 3: 80% (75% and 85% for each class respectively). By using a combined output at a higher-level decision module for the final prognostic evaluation, the total accuracy increases to 92% as explained below.

Having quantified the accuracy of each of the three modules for each of the two classes, the following connection weights are calculated for each of the three modules for class A:

-   Module 1: 0.88/2.28=0.39 -   Module 2: 0.65/2.28=0.28 -   Module 3: 0.75/2.28=0.33

The following weights are calculated for each of the three modules for class B:

-   Module 1: 0.92/2.62=0.35; -   Module 2: 0.85/2.62=0.325 -   Module 3: 0.85/2.62=0.325;

Based on the above coefficients, one calculates the accuracy of the classification of samples for each of the classes A and B, for example, 0.8 and 0.9.

The final decision in the third layer output element can be taken either as the maximum value between the calculated class-output for each class elements or by applying a calculated third layer coefficients for combining the class outputs as follows:

-   Class A—to third layer output coefficient: 0.8/(0.8+0.9)=0.47; -   Class B—to third layer output coefficient: 0.9/(0.8+0.9)=0.53;

If for a new input vector the corresponding output values for each class of each of the three modules are respectively: 0.6 and 0.5; 0.4 and 0.6; 0.5 and 0.5, the output value for class A is calculated as: (0.6×0.39)+(0.4×0.28)+(0.5×0.33)=0.51. The output value for class B is calculated as: (0.5×0.35)+(0.6×0.325)+(0.5×0.325)=0.53. The predicted outcome according to the maximizing strategy is class B.

According to the weighed output strategy, the output will be B again as output class A will get final evaluation of 0.51×0.47=0.24, and output class B will get an evaluation of: 0.53×0.53=0.28.

(3) In a third method, a combined system is interpreted as a multi-layer perceptron as described below and shown in FIG. 6. Optimal parameter values are calculated through a learning procedure utilising the error back-propagation algorithm (see for example the algorithm from p.276 [6]) also shown in FIG. 7.

Parameter values attached to the connections in a multi-layer perceptron (MLP) neural network structure (see the example shown in FIG. 3 b) are calculated as connection weights during training of the neural network.

The invention is also concerned with the discovery of patterns of gene expression in clusters of tissues (groups of samples that have a similar gene expression profiles) related to particular pattern of clinical parameters (e.g. low IPI, old age and low blood pressure). This is an important discovery because different clinical groups may have different patterns of gene expression that makes the process of finding common genes and drug targets for the whole population impossible. The method is described below:

Methods for Discovering Patterns of Genes Related to a Group of Patients Characterized by a Particular Clinical Parameter

One can also use an ECOS module with the combined input vector (N gene expression variables and M clinical variables) to derive patterns of gene expression as they relate to clinical findings. After a module is trained on data as described above, rules that represent clusters of data in the input space (which is the combined gene expression and clinical variables space) can be extracted as described above. As applied to a combined inputs module, these rules will have both gene variables and clinical variables that define the cluster of data samples expressed by this rule, thus uncovering the relationship between the genes and the clinical parameters as captured in the rule. For example: IF VAR1 = Gene1 is LOW (0.80) and VAR3 = Gene15 is HIGH (0.76) and VAR12 = Gene 32 is HIGH (0.91) and VAR25 = Age is LOW (0.80) and VAR31 = IPI is LOW (0.87) THEN CLASS Survive is VERY LIKELY (with a membership degree of 0.9), accommodated training examples in this rule are 7 out of 26. Radius of the cluster for this rule is 0.2. The above rule is interpreted as follows: for young people with low IPI, if gene 1 is low expressed and genes 15 and 32 are highly expressed, than the chances of the person to survive after the treatment are very high, measured as 90%.

FIG. 6 depicts a segment from the structure of a multiplayer perceptron (MLP). Using the structure shown in FIG. 6, one can implement an algorithm as follows: Forward pass: BF1. Apply an input vector x and its corresponding output vectory (the desired output). BF2. Propagate forward the input signals through all the neurons in all the layers and calculate the output signals. BF3. Calculate the Err_(j) for every output neuron j as for example: Err_(j) = y_(j)˜o_(j), where y_(j) is the jth element of the desired output vectory. Backward pass: BB1. Adjust the weights between the intermediate neurons i and output neurons j according to the calculated error Δw_(ij)(t + 1) = 1rate.o_(j)(1.o_(j)). Err_(j).o_(i) + momentum.Δw_(ij) (t) BB2. Calculate the error Err_(j) for neurons i in the intermediate layer: Err_(i) = Σ Err_(j).w_(ij) BB3. Propagate the error back to the neurons k of lower level: Δw_(ki)(t + 1) = 1rate.o_(j)(1.o_(j)). Err_(i).x_(k) + momentum.Δw_(ki).(t)

The descriptions and examples herein are intended to illustrate embodiments of the invention, and are not intended to be limiting to the scope of the invention. Other embodiments based on the descriptions, examples and Figures can be produced and practiced by those of ordinary skill in the art. All of those embodiments are considered to be part of this invention. All references cited herein are incorporated herein by reference in their entirety.

REFERENCES

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1. A medical decision support system, comprising: a processor; a memory device; an input for acquiring gene expression data, said input associated with a first classifier/predictor module; an input for acquiring clinical information, said input associated with a second classifier/predictor module; and a program available to said processor comprising a combination algorithm.
 2. The system of claim 1, further comprising an output device.
 3. A method for support a medical decision on a computer system, comprising the steps of: (a) classifying genetic expression information using a first classifier/predictor module to provide classified gene expression information; (b) classifying clinical information into a second classifier/predictor module to provide classified clinical information; and (c) combining said classified genetic information and said classified clinical information into a predicted outcome.
 4. The method of claim 3, wherein said steps (a) and (b) include at least one of an EFuNN process and a Bayesian process.
 5. A computer system to support a medical decision, comprising: a processor; and a memory device having classified gene expression information and classified clinical information stored thereon.
 6. The computer system of claim 5, further comprising a predicted outcome based on combined classified gene expression information and clinical information stored on said memory device.
 7. A method for extracting relationship rules between sets of genes and clinical variables common for patients of a group substantially as herein described. 